Overview

Dataset statistics

Number of variables33
Number of observations655309
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory170.0 MiB
Average record size in memory272.0 B

Variable types

Numeric19
Categorical14

Alerts

customer_age is highly overall correlated with date_of_birth_distinct_emails_4w and 1 other fieldsHigh correlation
velocity_24h is highly overall correlated with velocity_4w and 1 other fieldsHigh correlation
velocity_4w is highly overall correlated with velocity_24h and 1 other fieldsHigh correlation
credit_risk_score is highly overall correlated with proposed_credit_limitHigh correlation
proposed_credit_limit is highly overall correlated with credit_risk_scoreHigh correlation
month is highly overall correlated with velocity_24h and 1 other fieldsHigh correlation
date_of_birth_distinct_emails_4w is highly overall correlated with customer_ageHigh correlation
segmentacion_etaria is highly overall correlated with customer_ageHigh correlation
fraud_bool is highly imbalanced (92.6%)Imbalance
foreign_request is highly imbalanced (82.9%)Imbalance
source is highly imbalanced (95.9%)Imbalance
device_distinct_emails_8w is highly imbalanced (86.8%)Imbalance
id is uniformly distributedUniform
id has unique valuesUnique
x1 has unique valuesUnique
x2 has unique valuesUnique
bank_branch_count_8w has 14356 (2.2%) zerosZeros
month has 60584 (9.2%) zerosZeros

Reproduction

Analysis started2023-06-01 16:15:10.781818
Analysis finished2023-06-01 16:16:41.837621
Duration1 minute and 31.06 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct655309
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500065.35
Minimum0
Maximum999999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size10.0 MiB
2023-06-01T12:16:41.915235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49910.4
Q1250116
median500123
Q3749854
95-th percentile949801.6
Maximum999999
Range999999
Interquartile range (IQR)499738

Descriptive statistics

Standard deviation288622.61
Coefficient of variation (CV)0.57716977
Kurtosis-1.1998662
Mean500065.35
Median Absolute Deviation (MAD)249873
Skewness-0.00080945587
Sum3.2769733 × 1011
Variance8.3303008 × 1010
MonotonicityNot monotonic
2023-06-01T12:16:42.014698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149585 1
 
< 0.1%
377616 1
 
< 0.1%
438298 1
 
< 0.1%
484431 1
 
< 0.1%
664802 1
 
< 0.1%
318151 1
 
< 0.1%
492099 1
 
< 0.1%
27871 1
 
< 0.1%
893808 1
 
< 0.1%
504283 1
 
< 0.1%
Other values (655299) 655299
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
14 1
< 0.1%
ValueCountFrequency (%)
999999 1
< 0.1%
999998 1
< 0.1%
999997 1
< 0.1%
999996 1
< 0.1%
999994 1
< 0.1%
999993 1
< 0.1%
999992 1
< 0.1%
999991 1
< 0.1%
999988 1
< 0.1%
999987 1
< 0.1%

fraud_bool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
0.0
649392 
1.0
 
5917

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1965927
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 649392
99.1%
1.0 5917
 
0.9%

Length

2023-06-01T12:16:42.099104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-01T12:16:42.182187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 649392
99.1%
1.0 5917
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 1304701
66.4%
. 655309
33.3%
1 5917
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1310618
66.7%
Other Punctuation 655309
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1304701
99.5%
1 5917
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 655309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1965927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1304701
66.4%
. 655309
33.3%
1 5917
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1965927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1304701
66.4%
. 655309
33.3%
1 5917
 
0.3%

income
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5844864
Minimum0.1
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 MiB
2023-06-01T12:16:42.238768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.3
median0.7
Q30.8
95-th percentile0.9
Maximum0.9
Range0.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.28648911
Coefficient of variation (CV)0.4901553
Kurtosis-1.1799478
Mean0.5844864
Median Absolute Deviation (MAD)0.2
Skewness-0.49736004
Sum383019.2
Variance0.08207601
MonotonicityNot monotonic
2023-06-01T12:16:42.306444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.9 160482
24.5%
0.8 101452
15.5%
0.1 91398
13.9%
0.6 72312
11.0%
0.7 69766
10.6%
0.4 50932
 
7.8%
0.2 41631
 
6.4%
0.5 35804
 
5.5%
0.3 31532
 
4.8%
ValueCountFrequency (%)
0.1 91398
13.9%
0.2 41631
 
6.4%
0.3 31532
 
4.8%
0.4 50932
 
7.8%
0.5 35804
 
5.5%
0.6 72312
11.0%
0.7 69766
10.6%
0.8 101452
15.5%
0.9 160482
24.5%
ValueCountFrequency (%)
0.9 160482
24.5%
0.8 101452
15.5%
0.7 69766
10.6%
0.6 72312
11.0%
0.5 35804
 
5.5%
0.4 50932
 
7.8%
0.3 31532
 
4.8%
0.2 41631
 
6.4%
0.1 91398
13.9%
Distinct415
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.12352
Minimum0
Maximum429
Zeros4947
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size10.0 MiB
2023-06-01T12:16:42.397502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q129
median67
Q3160
95-th percentile306
Maximum429
Range429
Interquartile range (IQR)131

Descriptive statistics

Standard deviation94.629252
Coefficient of variation (CV)0.92661565
Kurtosis0.56170821
Mean102.12352
Median Absolute Deviation (MAD)53
Skewness1.1310549
Sum66922462
Variance8954.6954
MonotonicityNot monotonic
2023-06-01T12:16:42.484164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 8359
 
1.3%
7 8221
 
1.3%
8 8066
 
1.2%
9 7921
 
1.2%
5 7819
 
1.2%
4 7468
 
1.1%
10 7424
 
1.1%
3 7091
 
1.1%
11 7086
 
1.1%
2 6627
 
1.0%
Other values (405) 579227
88.4%
ValueCountFrequency (%)
0 4947
0.8%
1 5707
0.9%
2 6627
1.0%
3 7091
1.1%
4 7468
1.1%
5 7819
1.2%
6 8359
1.3%
7 8221
1.3%
8 8066
1.2%
9 7921
1.2%
ValueCountFrequency (%)
429 1
 
< 0.1%
423 1
 
< 0.1%
413 2
< 0.1%
412 1
 
< 0.1%
411 1
 
< 0.1%
410 2
< 0.1%
409 1
 
< 0.1%
408 3
< 0.1%
406 1
 
< 0.1%
405 4
< 0.1%

customer_age
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.656272
Minimum10
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 MiB
2023-06-01T12:16:42.556072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q130
median50
Q350
95-th percentile60
Maximum90
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.803813
Coefficient of variation (CV)0.33137418
Kurtosis-0.65501156
Mean41.656272
Median Absolute Deviation (MAD)10
Skewness-0.20448935
Sum27297730
Variance190.54526
MonotonicityNot monotonic
2023-06-01T12:16:42.621159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
50 255249
39.0%
30 121374
18.5%
20 94398
 
14.4%
40 92408
 
14.1%
60 67573
 
10.3%
70 13262
 
2.0%
10 7981
 
1.2%
80 2851
 
0.4%
90 213
 
< 0.1%
ValueCountFrequency (%)
10 7981
 
1.2%
20 94398
 
14.4%
30 121374
18.5%
40 92408
 
14.1%
50 255249
39.0%
60 67573
 
10.3%
70 13262
 
2.0%
80 2851
 
0.4%
90 213
 
< 0.1%
ValueCountFrequency (%)
90 213
 
< 0.1%
80 2851
 
0.4%
70 13262
 
2.0%
60 67573
 
10.3%
50 255249
39.0%
40 92408
 
14.1%
30 121374
18.5%
20 94398
 
14.4%
10 7981
 
1.2%

days_since_request
Real number (ℝ)

Distinct649978
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64836459
Minimum3.1127908 × 10-8
Maximum76.577505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 MiB
2023-06-01T12:16:42.712979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.1127908 × 10-8
5-th percentile0.0014993256
Q10.0076485128
median0.015946506
Q30.026767428
95-th percentile1.5263983
Maximum76.577505
Range76.577505
Interquartile range (IQR)0.019118915

Descriptive statistics

Standard deviation3.7488756
Coefficient of variation (CV)5.7820487
Kurtosis174.32988
Mean0.64836459
Median Absolute Deviation (MAD)0.0092164869
Skewness11.177248
Sum424879.15
Variance14.054069
MonotonicityNot monotonic
2023-06-01T12:16:42.804194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02779427789 3
 
< 0.1%
0.01752137533 3
 
< 0.1%
0.01274189252 3
 
< 0.1%
0.01743084263 3
 
< 0.1%
0.01030164521 3
 
< 0.1%
0.03287823666 3
 
< 0.1%
0.02882843571 3
 
< 0.1%
0.01715185264 3
 
< 0.1%
0.01318010777 3
 
< 0.1%
0.02032779054 3
 
< 0.1%
Other values (649968) 655279
> 99.9%
ValueCountFrequency (%)
3.112790756 × 10-81
< 0.1%
4.284949667 × 10-81
< 0.1%
6.467590399 × 10-81
< 0.1%
8.972571815 × 10-81
< 0.1%
1.936133843 × 10-71
< 0.1%
2.340225221 × 10-71
< 0.1%
2.459462076 × 10-71
< 0.1%
2.756645081 × 10-71
< 0.1%
4.231948539 × 10-71
< 0.1%
4.712836535 × 10-71
< 0.1%
ValueCountFrequency (%)
76.57750471 1
< 0.1%
76.44178416 1
< 0.1%
75.77016058 1
< 0.1%
75.45246817 1
< 0.1%
75.38991741 1
< 0.1%
75.35012786 1
< 0.1%
75.21939776 1
< 0.1%
75.18252938 1
< 0.1%
75.1514815 1
< 0.1%
75.09746385 1
< 0.1%

payment_type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
AB
338623 
AA
213822 
AD
88087 
AC
 
14589
AE
 
188

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1310618
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAA
2nd rowAB
3rd rowAB
4th rowAA
5th rowAA

Common Values

ValueCountFrequency (%)
AB 338623
51.7%
AA 213822
32.6%
AD 88087
 
13.4%
AC 14589
 
2.2%
AE 188
 
< 0.1%

Length

2023-06-01T12:16:42.891796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-01T12:16:42.973498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ab 338623
51.7%
aa 213822
32.6%
ad 88087
 
13.4%
ac 14589
 
2.2%
ae 188
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 869131
66.3%
B 338623
 
25.8%
D 88087
 
6.7%
C 14589
 
1.1%
E 188
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1310618
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 869131
66.3%
B 338623
 
25.8%
D 88087
 
6.7%
C 14589
 
1.1%
E 188
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1310618
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 869131
66.3%
B 338623
 
25.8%
D 88087
 
6.7%
C 14589
 
1.1%
E 188
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1310618
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 869131
66.3%
B 338623
 
25.8%
D 88087
 
6.7%
C 14589
 
1.1%
E 188
 
< 0.1%

zip_count_4w
Real number (ℝ)

Distinct6180
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1525.6851
Minimum1
Maximum6650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 MiB
2023-06-01T12:16:43.056719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile506
Q1894
median1207
Q31837
95-th percentile3601
Maximum6650
Range6649
Interquartile range (IQR)943

Descriptive statistics

Standard deviation974.96672
Coefficient of variation (CV)0.63903536
Kurtosis2.5327484
Mean1525.6851
Median Absolute Deviation (MAD)404
Skewness1.5808295
Sum9.9979517 × 108
Variance950560.11
MonotonicityNot monotonic
2023-06-01T12:16:43.144680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1062 605
 
0.1%
1023 601
 
0.1%
1021 601
 
0.1%
1014 601
 
0.1%
1054 597
 
0.1%
924 596
 
0.1%
1000 591
 
0.1%
1033 589
 
0.1%
1048 585
 
0.1%
1009 584
 
0.1%
Other values (6170) 649359
99.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 5
< 0.1%
3 3
< 0.1%
4 1
 
< 0.1%
5 4
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
10 5
< 0.1%
ValueCountFrequency (%)
6650 1
< 0.1%
6593 1
< 0.1%
6557 1
< 0.1%
6553 1
< 0.1%
6526 2
< 0.1%
6513 1
< 0.1%
6506 1
< 0.1%
6501 1
< 0.1%
6442 1
< 0.1%
6435 1
< 0.1%

velocity_6h
Real number (ℝ)

Distinct654701
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5522.1329
Minimum-174.10969
Maximum16754.959
Zeros0
Zeros (%)0.0%
Negative37
Negative (%)< 0.1%
Memory size10.0 MiB
2023-06-01T12:16:43.240839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-174.10969
5-th percentile1179.1008
Q13312.516
median5186.6271
Q37515.9797
95-th percentile11075.32
Maximum16754.959
Range16929.069
Interquartile range (IQR)4203.4637

Descriptive statistics

Standard deviation3002.3261
Coefficient of variation (CV)0.54368958
Kurtosis0.04996197
Mean5522.1329
Median Absolute Deviation (MAD)2077.4695
Skewness0.578512
Sum3.6187034 × 109
Variance9013962
MonotonicityNot monotonic
2023-06-01T12:16:43.328907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5869.79409 3
 
< 0.1%
9452.454217 2
 
< 0.1%
3461.012226 2
 
< 0.1%
5521.13977 2
 
< 0.1%
6617.097227 2
 
< 0.1%
5125.394232 2
 
< 0.1%
4591.141526 2
 
< 0.1%
5704.343728 2
 
< 0.1%
2739.77735 2
 
< 0.1%
5559.489189 2
 
< 0.1%
Other values (654691) 655288
> 99.9%
ValueCountFrequency (%)
-174.1096908 1
< 0.1%
-155.4307304 1
< 0.1%
-130.456928 1
< 0.1%
-113.0468992 1
< 0.1%
-110.7034762 1
< 0.1%
-106.9782971 1
< 0.1%
-96.51829979 1
< 0.1%
-84.13861148 1
< 0.1%
-77.95925234 1
< 0.1%
-75.12062215 1
< 0.1%
ValueCountFrequency (%)
16754.95902 1
< 0.1%
16754.20092 1
< 0.1%
16701.86995 1
< 0.1%
16573.28576 1
< 0.1%
16517.25703 1
< 0.1%
16515.61382 1
< 0.1%
16490.82418 1
< 0.1%
16472.27084 1
< 0.1%
16458.71349 1
< 0.1%
16455.16045 1
< 0.1%

velocity_24h
Real number (ℝ)

Distinct654885
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4681.3728
Minimum1322.3252
Maximum9511.5441
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 MiB
2023-06-01T12:16:43.426536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1322.3252
5-th percentile2553.8804
Q13498.6928
median4643.0277
Q35631.8026
95-th percentile7330.8154
Maximum9511.5441
Range8189.2189
Interquartile range (IQR)2133.1098

Descriptive statistics

Standard deviation1475.9684
Coefficient of variation (CV)0.31528539
Kurtosis-0.25714099
Mean4681.3728
Median Absolute Deviation (MAD)1072.6331
Skewness0.42768422
Sum3.0677457 × 109
Variance2178482.9
MonotonicityNot monotonic
2023-06-01T12:16:43.509430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5083.929288 3
 
< 0.1%
5027.707936 2
 
< 0.1%
4392.403126 2
 
< 0.1%
4579.845119 2
 
< 0.1%
2744.179591 2
 
< 0.1%
4680.467174 2
 
< 0.1%
4536.599507 2
 
< 0.1%
3454.284936 2
 
< 0.1%
4175.190245 2
 
< 0.1%
5221.650602 2
 
< 0.1%
Other values (654875) 655288
> 99.9%
ValueCountFrequency (%)
1322.325176 1
< 0.1%
1326.681151 1
< 0.1%
1346.622214 1
< 0.1%
1366.255299 1
< 0.1%
1375.367356 1
< 0.1%
1375.998099 1
< 0.1%
1380.047025 1
< 0.1%
1394.837472 1
< 0.1%
1401.913143 1
< 0.1%
1405.763718 1
< 0.1%
ValueCountFrequency (%)
9511.544062 1
< 0.1%
9505.181514 1
< 0.1%
9474.913485 1
< 0.1%
9472.491584 1
< 0.1%
9453.246771 1
< 0.1%
9449.585484 1
< 0.1%
9447.698906 1
< 0.1%
9446.485797 1
< 0.1%
9439.197009 1
< 0.1%
9435.635329 1
< 0.1%

velocity_4w
Real number (ℝ)

Distinct654540
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4746.2979
Minimum2870.5916
Maximum7019.201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 MiB
2023-06-01T12:16:43.600432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2870.5916
5-th percentile3116.549
Q14239.9849
median4808.447
Q35335.051
95-th percentile6362.124
Maximum7019.201
Range4148.6094
Interquartile range (IQR)1095.0661

Descriptive statistics

Standard deviation884.09839
Coefficient of variation (CV)0.18627116
Kurtosis-0.26460705
Mean4746.2979
Median Absolute Deviation (MAD)555.58223
Skewness0.046026068
Sum3.1102917 × 109
Variance781629.96
MonotonicityNot monotonic
2023-06-01T12:16:43.867907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3480.307357 3
 
< 0.1%
4358.685619 3
 
< 0.1%
3135.31079 2
 
< 0.1%
4966.784353 2
 
< 0.1%
4348.069414 2
 
< 0.1%
4843.60264 2
 
< 0.1%
4822.915805 2
 
< 0.1%
4943.825256 2
 
< 0.1%
5470.681186 2
 
< 0.1%
5194.720472 2
 
< 0.1%
Other values (654530) 655287
> 99.9%
ValueCountFrequency (%)
2870.591613 1
< 0.1%
2896.415063 1
< 0.1%
2918.632222 1
< 0.1%
2919.806878 1
< 0.1%
2920.415951 1
< 0.1%
2921.590392 1
< 0.1%
2922.163499 1
< 0.1%
2926.560489 1
< 0.1%
2928.591401 1
< 0.1%
2933.821361 1
< 0.1%
ValueCountFrequency (%)
7019.20103 1
< 0.1%
6977.711782 1
< 0.1%
6970.521948 1
< 0.1%
6961.908358 1
< 0.1%
6955.383287 1
< 0.1%
6942.150889 1
< 0.1%
6941.960129 1
< 0.1%
6940.29743 1
< 0.1%
6938.88494 1
< 0.1%
6938.768203 1
< 0.1%

bank_branch_count_8w
Real number (ℝ)

Distinct2317
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263.60399
Minimum0
Maximum2377
Zeros14356
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size10.0 MiB
2023-06-01T12:16:43.955671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median14
Q399
95-th percentile1659
Maximum2377
Range2377
Interquartile range (IQR)92

Descriptive statistics

Standard deviation526.90827
Coefficient of variation (CV)1.998863
Kurtosis3.2446548
Mean263.60399
Median Absolute Deviation (MAD)12
Skewness2.108835
Sum1.7274207 × 108
Variance277632.32
MonotonicityNot monotonic
2023-06-01T12:16:44.042136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 49909
 
7.6%
2 40709
 
6.2%
11 25215
 
3.8%
12 25041
 
3.8%
10 24684
 
3.8%
13 23834
 
3.6%
9 22944
 
3.5%
14 21637
 
3.3%
8 20653
 
3.2%
15 18619
 
2.8%
Other values (2307) 382064
58.3%
ValueCountFrequency (%)
0 14356
 
2.2%
1 49909
7.6%
2 40709
6.2%
3 13121
 
2.0%
4 10590
 
1.6%
5 12714
 
1.9%
6 15431
 
2.4%
7 18100
 
2.8%
8 20653
3.2%
9 22944
3.5%
ValueCountFrequency (%)
2377 1
< 0.1%
2360 1
< 0.1%
2357 1
< 0.1%
2355 2
< 0.1%
2348 1
< 0.1%
2347 1
< 0.1%
2346 1
< 0.1%
2345 1
< 0.1%
2342 1
< 0.1%
2339 1
< 0.1%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
CA
445892 
CB
95464 
CC
57443 
CF
 
27978
CD
 
15049
Other values (2)
 
13483

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1310618
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCF
2nd rowCC
3rd rowCA
4th rowCC
5th rowCA

Common Values

ValueCountFrequency (%)
CA 445892
68.0%
CB 95464
 
14.6%
CC 57443
 
8.8%
CF 27978
 
4.3%
CD 15049
 
2.3%
CE 13132
 
2.0%
CG 351
 
0.1%

Length

2023-06-01T12:16:44.119728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-01T12:16:44.204301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ca 445892
68.0%
cb 95464
 
14.6%
cc 57443
 
8.8%
cf 27978
 
4.3%
cd 15049
 
2.3%
ce 13132
 
2.0%
cg 351
 
0.1%

Most occurring characters

ValueCountFrequency (%)
C 712752
54.4%
A 445892
34.0%
B 95464
 
7.3%
F 27978
 
2.1%
D 15049
 
1.1%
E 13132
 
1.0%
G 351
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1310618
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 712752
54.4%
A 445892
34.0%
B 95464
 
7.3%
F 27978
 
2.1%
D 15049
 
1.1%
E 13132
 
1.0%
G 351
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1310618
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 712752
54.4%
A 445892
34.0%
B 95464
 
7.3%
F 27978
 
2.1%
D 15049
 
1.1%
E 13132
 
1.0%
G 351
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1310618
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 712752
54.4%
A 445892
34.0%
B 95464
 
7.3%
F 27978
 
2.1%
D 15049
 
1.1%
E 13132
 
1.0%
G 351
 
< 0.1%

credit_risk_score
Real number (ℝ)

Distinct537
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.27306
Minimum-164
Maximum387
Zeros292
Zeros (%)< 0.1%
Negative8321
Negative (%)1.3%
Memory size10.0 MiB
2023-06-01T12:16:44.290701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-164
5-th percentile32
Q189
median130
Q3189
95-th percentile267
Maximum387
Range551
Interquartile range (IQR)100

Descriptive statistics

Standard deviation72.161074
Coefficient of variation (CV)0.51812657
Kurtosis-0.068336635
Mean139.27306
Median Absolute Deviation (MAD)49
Skewness0.26229859
Sum91266891
Variance5207.2207
MonotonicityNot monotonic
2023-06-01T12:16:44.371637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 4267
 
0.7%
108 4266
 
0.7%
115 4256
 
0.6%
113 4237
 
0.6%
116 4224
 
0.6%
112 4214
 
0.6%
109 4156
 
0.6%
106 4145
 
0.6%
119 4131
 
0.6%
107 4131
 
0.6%
Other values (527) 613282
93.6%
ValueCountFrequency (%)
-164 2
 
< 0.1%
-160 1
 
< 0.1%
-157 2
 
< 0.1%
-154 1
 
< 0.1%
-152 1
 
< 0.1%
-150 5
< 0.1%
-147 1
 
< 0.1%
-146 2
 
< 0.1%
-145 2
 
< 0.1%
-144 2
 
< 0.1%
ValueCountFrequency (%)
387 3
< 0.1%
386 3
< 0.1%
383 2
 
< 0.1%
382 1
 
< 0.1%
380 3
< 0.1%
378 2
 
< 0.1%
377 7
< 0.1%
376 5
< 0.1%
375 3
< 0.1%
374 6
< 0.1%

housing_status
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
BC
222464 
BB
188049 
BA
141020 
BE
85261 
BD
 
17142
Other values (2)
 
1373

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1310618
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBA
2nd rowBA
3rd rowBE
4th rowBB
5th rowBA

Common Values

ValueCountFrequency (%)
BC 222464
33.9%
BB 188049
28.7%
BA 141020
21.5%
BE 85261
 
13.0%
BD 17142
 
2.6%
BF 1170
 
0.2%
BG 203
 
< 0.1%

Length

2023-06-01T12:16:44.451176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-01T12:16:44.536382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bc 222464
33.9%
bb 188049
28.7%
ba 141020
21.5%
be 85261
 
13.0%
bd 17142
 
2.6%
bf 1170
 
0.2%
bg 203
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
B 843358
64.3%
C 222464
 
17.0%
A 141020
 
10.8%
E 85261
 
6.5%
D 17142
 
1.3%
F 1170
 
0.1%
G 203
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1310618
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 843358
64.3%
C 222464
 
17.0%
A 141020
 
10.8%
E 85261
 
6.5%
D 17142
 
1.3%
F 1170
 
0.1%
G 203
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1310618
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 843358
64.3%
C 222464
 
17.0%
A 141020
 
10.8%
E 85261
 
6.5%
D 17142
 
1.3%
F 1170
 
0.1%
G 203
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1310618
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 843358
64.3%
C 222464
 
17.0%
A 141020
 
10.8%
E 85261
 
6.5%
D 17142
 
1.3%
F 1170
 
0.1%
G 203
 
< 0.1%

bank_months_count
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.130474
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 MiB
2023-06-01T12:16:44.618912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median15
Q328
95-th percentile31
Maximum32
Range31
Interquartile range (IQR)26

Descriptive statistics

Standard deviation11.446715
Coefficient of variation (CV)0.75653377
Kurtosis-1.6079392
Mean15.130474
Median Absolute Deviation (MAD)13
Skewness0.018832125
Sum9915136
Variance131.02728
MonotonicityNot monotonic
2023-06-01T12:16:44.695384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 156649
23.9%
28 70611
10.8%
15 52374
 
8.0%
30 49089
 
7.5%
31 41224
 
6.3%
10 38382
 
5.9%
25 36953
 
5.6%
5 27821
 
4.2%
20 27015
 
4.1%
21 23558
 
3.6%
Other values (22) 131633
20.1%
ValueCountFrequency (%)
1 156649
23.9%
2 23215
 
3.5%
3 6996
 
1.1%
4 3960
 
0.6%
5 27821
 
4.2%
6 16103
 
2.5%
7 689
 
0.1%
8 39
 
< 0.1%
9 5282
 
0.8%
10 38382
 
5.9%
ValueCountFrequency (%)
32 18
 
< 0.1%
31 41224
6.3%
30 49089
7.5%
29 8508
 
1.3%
28 70611
10.8%
27 3947
 
0.6%
26 21612
 
3.3%
25 36953
5.6%
24 1593
 
0.2%
23 222
 
< 0.1%

has_other_cards
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
0.0
482357 
1.0
172952 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1965927
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 482357
73.6%
1.0 172952
 
26.4%

Length

2023-06-01T12:16:44.773947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-01T12:16:44.847896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 482357
73.6%
1.0 172952
 
26.4%

Most occurring characters

ValueCountFrequency (%)
0 1137666
57.9%
. 655309
33.3%
1 172952
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1310618
66.7%
Other Punctuation 655309
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1137666
86.8%
1 172952
 
13.2%
Other Punctuation
ValueCountFrequency (%)
. 655309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1965927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1137666
57.9%
. 655309
33.3%
1 172952
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1965927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1137666
57.9%
. 655309
33.3%
1 172952
 
8.8%

proposed_credit_limit
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean561.30841
Minimum200
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 MiB
2023-06-01T12:16:44.906527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile200
Q1200
median200
Q31000
95-th percentile1500
Maximum2000
Range1800
Interquartile range (IQR)800

Descriptive statistics

Standard deviation511.46581
Coefficient of variation (CV)0.91120283
Kurtosis-0.38423456
Mean561.30841
Median Absolute Deviation (MAD)0
Skewness1.0957626
Sum3.6783045 × 108
Variance261597.27
MonotonicityNot monotonic
2023-06-01T12:16:44.970586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
200 372802
56.9%
1500 114766
 
17.5%
500 95939
 
14.6%
1000 56746
 
8.7%
510 5358
 
0.8%
2000 4402
 
0.7%
990 4144
 
0.6%
490 491
 
0.1%
210 432
 
0.1%
1900 229
 
< 0.1%
ValueCountFrequency (%)
200 372802
56.9%
210 432
 
0.1%
490 491
 
0.1%
500 95939
 
14.6%
510 5358
 
0.8%
990 4144
 
0.6%
1000 56746
 
8.7%
1500 114766
 
17.5%
1900 229
 
< 0.1%
2000 4402
 
0.7%
ValueCountFrequency (%)
2000 4402
 
0.7%
1900 229
 
< 0.1%
1500 114766
 
17.5%
1000 56746
 
8.7%
990 4144
 
0.6%
510 5358
 
0.8%
500 95939
 
14.6%
490 491
 
0.1%
210 432
 
0.1%
200 372802
56.9%

foreign_request
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
0.0
638579 
1.0
 
16730

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1965927
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 638579
97.4%
1.0 16730
 
2.6%

Length

2023-06-01T12:16:45.048664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-01T12:16:45.121806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 638579
97.4%
1.0 16730
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 1293888
65.8%
. 655309
33.3%
1 16730
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1310618
66.7%
Other Punctuation 655309
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1293888
98.7%
1 16730
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 655309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1965927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1293888
65.8%
. 655309
33.3%
1 16730
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1965927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1293888
65.8%
. 655309
33.3%
1 16730
 
0.9%

source
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
INTERNET
652420 
TELEAPP
 
2889

Length

Max length8
Median length8
Mean length7.9955914
Min length7

Characters and Unicode

Total characters5239583
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINTERNET
2nd rowINTERNET
3rd rowINTERNET
4th rowINTERNET
5th rowINTERNET

Common Values

ValueCountFrequency (%)
INTERNET 652420
99.6%
TELEAPP 2889
 
0.4%

Length

2023-06-01T12:16:45.185510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-01T12:16:45.261072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
internet 652420
99.6%
teleapp 2889
 
0.4%

Most occurring characters

ValueCountFrequency (%)
E 1310618
25.0%
T 1307729
25.0%
N 1304840
24.9%
I 652420
12.5%
R 652420
12.5%
P 5778
 
0.1%
L 2889
 
0.1%
A 2889
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5239583
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1310618
25.0%
T 1307729
25.0%
N 1304840
24.9%
I 652420
12.5%
R 652420
12.5%
P 5778
 
0.1%
L 2889
 
0.1%
A 2889
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 5239583
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1310618
25.0%
T 1307729
25.0%
N 1304840
24.9%
I 652420
12.5%
R 652420
12.5%
P 5778
 
0.1%
L 2889
 
0.1%
A 2889
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5239583
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1310618
25.0%
T 1307729
25.0%
N 1304840
24.9%
I 652420
12.5%
R 652420
12.5%
P 5778
 
0.1%
L 2889
 
0.1%
A 2889
 
0.1%

session_length_in_minutes
Real number (ℝ)

Distinct653336
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7447408
Minimum-1
Maximum82.308687
Zeros0
Zeros (%)0.0%
Negative693
Negative (%)0.1%
Memory size10.0 MiB
2023-06-01T12:16:45.336971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.2871378
Q13.1776163
median5.1950285
Q39.3328318
95-th percentile21.706213
Maximum82.308687
Range83.308687
Interquartile range (IQR)6.1552155

Descriptive statistics

Standard deviation8.0835956
Coefficient of variation (CV)1.0437529
Kurtosis14.169298
Mean7.7447408
Median Absolute Deviation (MAD)2.658938
Skewness3.1868406
Sum5075198.3
Variance65.344519
MonotonicityNot monotonic
2023-06-01T12:16:45.421895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 693
 
0.1%
8.993731516 3
 
< 0.1%
4.455781343 3
 
< 0.1%
4.706080005 3
 
< 0.1%
4.515706471 2
 
< 0.1%
4.532371545 2
 
< 0.1%
2.402312558 2
 
< 0.1%
4.744296215 2
 
< 0.1%
4.82211935 2
 
< 0.1%
13.5890956 2
 
< 0.1%
Other values (653326) 654595
99.9%
ValueCountFrequency (%)
-1 693
0.1%
4.088611726 × 10-51
 
< 0.1%
0.001224497171 1
 
< 0.1%
0.001894059749 1
 
< 0.1%
0.003472779563 1
 
< 0.1%
0.004286417425 1
 
< 0.1%
0.006187519682 1
 
< 0.1%
0.007119304742 1
 
< 0.1%
0.007424412727 1
 
< 0.1%
0.008876160022 1
 
< 0.1%
ValueCountFrequency (%)
82.30868693 1
< 0.1%
81.95029589 1
< 0.1%
81.83908038 1
< 0.1%
81.70899171 1
< 0.1%
81.19897617 1
< 0.1%
80.82914753 1
< 0.1%
80.64418021 1
< 0.1%
80.23491256 1
< 0.1%
80.09767418 1
< 0.1%
79.42067157 1
< 0.1%

device_os
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
linux
216598 
windows
204072 
other
194695 
macintosh
34242 
x11
 
5702

Length

Max length9
Median length5
Mean length5.8144372
Min length3

Characters and Unicode

Total characters3810253
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlinux
2nd rowlinux
3rd rowwindows
4th rowlinux
5th rowlinux

Common Values

ValueCountFrequency (%)
linux 216598
33.1%
windows 204072
31.1%
other 194695
29.7%
macintosh 34242
 
5.2%
x11 5702
 
0.9%

Length

2023-06-01T12:16:45.502684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-01T12:16:45.586071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
linux 216598
33.1%
windows 204072
31.1%
other 194695
29.7%
macintosh 34242
 
5.2%
x11 5702
 
0.9%

Most occurring characters

ValueCountFrequency (%)
i 454912
11.9%
n 454912
11.9%
o 433009
11.4%
w 408144
10.7%
s 238314
 
6.3%
h 228937
 
6.0%
t 228937
 
6.0%
x 222300
 
5.8%
l 216598
 
5.7%
u 216598
 
5.7%
Other values (7) 707592
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3798849
99.7%
Decimal Number 11404
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 454912
12.0%
n 454912
12.0%
o 433009
11.4%
w 408144
10.7%
s 238314
 
6.3%
h 228937
 
6.0%
t 228937
 
6.0%
x 222300
 
5.9%
l 216598
 
5.7%
u 216598
 
5.7%
Other values (6) 696188
18.3%
Decimal Number
ValueCountFrequency (%)
1 11404
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3798849
99.7%
Common 11404
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 454912
12.0%
n 454912
12.0%
o 433009
11.4%
w 408144
10.7%
s 238314
 
6.3%
h 228937
 
6.0%
t 228937
 
6.0%
x 222300
 
5.9%
l 216598
 
5.7%
u 216598
 
5.7%
Other values (6) 696188
18.3%
Common
ValueCountFrequency (%)
1 11404
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3810253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 454912
11.9%
n 454912
11.9%
o 433009
11.4%
w 408144
10.7%
s 238314
 
6.3%
h 228937
 
6.0%
t 228937
 
6.0%
x 222300
 
5.8%
l 216598
 
5.7%
u 216598
 
5.7%
Other values (7) 707592
18.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
1.0
366601 
0.0
288708 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1965927
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 366601
55.9%
0.0 288708
44.1%

Length

2023-06-01T12:16:45.660745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-01T12:16:45.734425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 366601
55.9%
0.0 288708
44.1%

Most occurring characters

ValueCountFrequency (%)
0 944017
48.0%
. 655309
33.3%
1 366601
 
18.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1310618
66.7%
Other Punctuation 655309
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 944017
72.0%
1 366601
 
28.0%
Other Punctuation
ValueCountFrequency (%)
. 655309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1965927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 944017
48.0%
. 655309
33.3%
1 366601
 
18.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1965927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 944017
48.0%
. 655309
33.3%
1 366601
 
18.6%

month
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6376839
Minimum0
Maximum7
Zeros60584
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size10.0 MiB
2023-06-01T12:16:45.790218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1424495
Coefficient of variation (CV)0.58895978
Kurtosis-1.0963959
Mean3.6376839
Median Absolute Deviation (MAD)2
Skewness-0.086185367
Sum2383807
Variance4.5900899
MonotonicityNot monotonic
2023-06-01T12:16:45.854049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 101764
15.5%
5 100401
15.3%
6 90334
13.8%
2 86962
13.3%
4 80458
12.3%
1 67482
10.3%
7 67324
10.3%
0 60584
9.2%
ValueCountFrequency (%)
0 60584
9.2%
1 67482
10.3%
2 86962
13.3%
3 101764
15.5%
4 80458
12.3%
5 100401
15.3%
6 90334
13.8%
7 67324
10.3%
ValueCountFrequency (%)
7 67324
10.3%
6 90334
13.8%
5 100401
15.3%
4 80458
12.3%
3 101764
15.5%
2 86962
13.3%
1 67482
10.3%
0 60584
9.2%

x1
Real number (ℝ)

Distinct655309
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01072629
Minimum-4.9778644
Maximum5.9384928
Zeros0
Zeros (%)0.0%
Negative326592
Negative (%)49.8%
Memory size10.0 MiB
2023-06-01T12:16:45.944485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4.9778644
5-th percentile-1.6381489
Q1-0.66932602
median0.0041363717
Q30.68332727
95-th percentile1.6757838
Maximum5.9384928
Range10.916357
Interquartile range (IQR)1.3526533

Descriptive statistics

Standard deviation1.0104988
Coefficient of variation (CV)94.207669
Kurtosis0.12276264
Mean0.01072629
Median Absolute Deviation (MAD)0.67632183
Skewness0.0528772
Sum7029.0342
Variance1.0211078
MonotonicityNot monotonic
2023-06-01T12:16:46.032378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.009335806623 1
 
< 0.1%
-0.8088673762 1
 
< 0.1%
0.8148363761 1
 
< 0.1%
1.708443345 1
 
< 0.1%
0.8614519241 1
 
< 0.1%
0.4025103591 1
 
< 0.1%
-0.7386674103 1
 
< 0.1%
0.2069842686 1
 
< 0.1%
-0.00443711277 1
 
< 0.1%
0.5441622779 1
 
< 0.1%
Other values (655299) 655299
> 99.9%
ValueCountFrequency (%)
-4.977864446 1
< 0.1%
-4.913331616 1
< 0.1%
-4.852117653 1
< 0.1%
-4.659952967 1
< 0.1%
-4.602974619 1
< 0.1%
-4.446632241 1
< 0.1%
-4.371314395 1
< 0.1%
-4.365340579 1
< 0.1%
-4.233164797 1
< 0.1%
-4.215015696 1
< 0.1%
ValueCountFrequency (%)
5.938492819 1
< 0.1%
5.647253063 1
< 0.1%
5.518075554 1
< 0.1%
5.497664426 1
< 0.1%
5.481411648 1
< 0.1%
5.35703142 1
< 0.1%
5.335605496 1
< 0.1%
5.187241498 1
< 0.1%
5.112475868 1
< 0.1%
5.031137736 1
< 0.1%

x2
Real number (ℝ)

Distinct655309
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0088502104
Minimum-4.7833695
Maximum5.1129779
Zeros0
Zeros (%)0.0%
Negative326378
Negative (%)49.8%
Memory size10.0 MiB
2023-06-01T12:16:46.126452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4.7833695
5-th percentile-1.6396113
Q1-0.67100547
median0.0048348539
Q30.68131509
95-th percentile1.6724096
Maximum5.1129779
Range9.8963474
Interquartile range (IQR)1.3523206

Descriptive statistics

Standard deviation1.0090708
Coefficient of variation (CV)114.01659
Kurtosis0.11578228
Mean0.0088502104
Median Absolute Deviation (MAD)0.67618021
Skewness0.050105686
Sum5799.6225
Variance1.0182239
MonotonicityNot monotonic
2023-06-01T12:16:46.212569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.096682187 1
 
< 0.1%
-0.2377910703 1
 
< 0.1%
1.430172894 1
 
< 0.1%
-0.8312503954 1
 
< 0.1%
-0.308934278 1
 
< 0.1%
1.142870945 1
 
< 0.1%
-0.4006949142 1
 
< 0.1%
1.753921762 1
 
< 0.1%
2.169011833 1
 
< 0.1%
0.7014442829 1
 
< 0.1%
Other values (655299) 655299
> 99.9%
ValueCountFrequency (%)
-4.783369547 1
< 0.1%
-4.618998589 1
< 0.1%
-4.433939506 1
< 0.1%
-4.426691766 1
< 0.1%
-4.3132024 1
< 0.1%
-4.302575372 1
< 0.1%
-4.28748851 1
< 0.1%
-4.257022333 1
< 0.1%
-4.252600076 1
< 0.1%
-4.219491273 1
< 0.1%
ValueCountFrequency (%)
5.1129779 1
< 0.1%
5.086379754 1
< 0.1%
5.040554019 1
< 0.1%
4.996511552 1
< 0.1%
4.971527753 1
< 0.1%
4.92081972 1
< 0.1%
4.911469175 1
< 0.1%
4.846981941 1
< 0.1%
4.832154593 1
< 0.1%
4.783761934 1
< 0.1%

name_email_similarity
Real number (ℝ)

Distinct654736
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48546237
Minimum6.1167532 × 10-6
Maximum0.99999996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 MiB
2023-06-01T12:16:46.303776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.1167532 × 10-6
5-th percentile0.063314363
Q10.2002637
median0.49153936
Q30.75763414
95-th percentile0.91615415
Maximum0.99999996
Range0.99999385
Interquartile range (IQR)0.55737044

Descriptive statistics

Standard deviation0.2959337
Coefficient of variation (CV)0.60959142
Kurtosis-1.3541708
Mean0.48546237
Median Absolute Deviation (MAD)0.27941391
Skewness0.054188053
Sum318127.86
Variance0.087576754
MonotonicityNot monotonic
2023-06-01T12:16:46.395212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4822098433 2
 
< 0.1%
0.7354668168 2
 
< 0.1%
0.8601710888 2
 
< 0.1%
0.3553535496 2
 
< 0.1%
0.9974528931 2
 
< 0.1%
0.07995081326 2
 
< 0.1%
0.1001204684 2
 
< 0.1%
0.9932400897 2
 
< 0.1%
0.1254527001 2
 
< 0.1%
0.8867180445 2
 
< 0.1%
Other values (654726) 655289
> 99.9%
ValueCountFrequency (%)
6.11675322 × 10-61
< 0.1%
8.85821685 × 10-61
< 0.1%
1.887405922 × 10-51
< 0.1%
2.022443363 × 10-51
< 0.1%
2.116087418 × 10-51
< 0.1%
2.539521405 × 10-51
< 0.1%
3.198422688 × 10-51
< 0.1%
4.252257594 × 10-51
< 0.1%
5.490666288 × 10-51
< 0.1%
5.587703243 × 10-51
< 0.1%
ValueCountFrequency (%)
0.9999999633 1
< 0.1%
0.9999998641 1
< 0.1%
0.9999996731 1
< 0.1%
0.9999990265 1
< 0.1%
0.9999984639 1
< 0.1%
0.9999982377 1
< 0.1%
0.9999981359 1
< 0.1%
0.9999977445 1
< 0.1%
0.9999976263 1
< 0.1%
0.9999974194 1
< 0.1%
Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7566781
Minimum0
Maximum39
Zeros2178
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size10.0 MiB
2023-06-01T12:16:46.482743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median7
Q311
95-th percentile17
Maximum39
Range39
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.8582247
Coefficient of variation (CV)0.626328
Kurtosis0.90328988
Mean7.7566781
Median Absolute Deviation (MAD)3
Skewness1.0093543
Sum5083021
Variance23.602347
MonotonicityNot monotonic
2023-06-01T12:16:46.568891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
5 74842
11.4%
6 66602
10.2%
4 62827
 
9.6%
7 57441
 
8.8%
3 48738
 
7.4%
2 46570
 
7.1%
8 45557
 
7.0%
11 33180
 
5.1%
9 32286
 
4.9%
10 26465
 
4.0%
Other values (30) 160801
24.5%
ValueCountFrequency (%)
0 2178
 
0.3%
1 21678
 
3.3%
2 46570
7.1%
3 48738
7.4%
4 62827
9.6%
5 74842
11.4%
6 66602
10.2%
7 57441
8.8%
8 45557
7.0%
9 32286
4.9%
ValueCountFrequency (%)
39 1
 
< 0.1%
38 2
 
< 0.1%
37 6
 
< 0.1%
36 8
 
< 0.1%
35 20
 
< 0.1%
34 29
 
< 0.1%
33 34
 
< 0.1%
32 66
< 0.1%
31 100
< 0.1%
30 128
< 0.1%

email_is_free
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
1.0
341237 
0.0
314072 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1965927
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 341237
52.1%
0.0 314072
47.9%

Length

2023-06-01T12:16:46.655318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-01T12:16:46.729313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 341237
52.1%
0.0 314072
47.9%

Most occurring characters

ValueCountFrequency (%)
0 969381
49.3%
. 655309
33.3%
1 341237
 
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1310618
66.7%
Other Punctuation 655309
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969381
74.0%
1 341237
 
26.0%
Other Punctuation
ValueCountFrequency (%)
. 655309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1965927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969381
49.3%
. 655309
33.3%
1 341237
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1965927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969381
49.3%
. 655309
33.3%
1 341237
 
17.4%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
1.0
635823 
2.0
 
16958
0.0
 
2528

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1965927
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 635823
97.0%
2.0 16958
 
2.6%
0.0 2528
 
0.4%

Length

2023-06-01T12:16:46.793360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-01T12:16:46.868974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 635823
97.0%
2.0 16958
 
2.6%
0.0 2528
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 657837
33.5%
. 655309
33.3%
1 635823
32.3%
2 16958
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1310618
66.7%
Other Punctuation 655309
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 657837
50.2%
1 635823
48.5%
2 16958
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 655309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1965927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 657837
33.5%
. 655309
33.3%
1 635823
32.3%
2 16958
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1965927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 657837
33.5%
. 655309
33.3%
1 635823
32.3%
2 16958
 
0.9%

phone_home_valid
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
1.0
336915 
0.0
318394 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1965927
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 336915
51.4%
0.0 318394
48.6%

Length

2023-06-01T12:16:46.932658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-01T12:16:47.007897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 336915
51.4%
0.0 318394
48.6%

Most occurring characters

ValueCountFrequency (%)
0 973703
49.5%
. 655309
33.3%
1 336915
 
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1310618
66.7%
Other Punctuation 655309
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 973703
74.3%
1 336915
 
25.7%
Other Punctuation
ValueCountFrequency (%)
. 655309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1965927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 973703
49.5%
. 655309
33.3%
1 336915
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1965927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 973703
49.5%
. 655309
33.3%
1 336915
 
17.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
1.0
565342 
0.0
89967 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1965927
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 565342
86.3%
0.0 89967
 
13.7%

Length

2023-06-01T12:16:47.070515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-01T12:16:47.144150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 565342
86.3%
0.0 89967
 
13.7%

Most occurring characters

ValueCountFrequency (%)
0 745276
37.9%
. 655309
33.3%
1 565342
28.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1310618
66.7%
Other Punctuation 655309
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 745276
56.9%
1 565342
43.1%
Other Punctuation
ValueCountFrequency (%)
. 655309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1965927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 745276
37.9%
. 655309
33.3%
1 565342
28.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1965927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 745276
37.9%
. 655309
33.3%
1 565342
28.8%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
Adulto
469031 
Adulto-Joven
94398 
Persona Mayor
83899 
Joven
 
7981

Length

Max length13
Median length6
Mean length7.7483355
Min length5

Characters and Unicode

Total characters5077554
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdulto
2nd rowAdulto
3rd rowAdulto
4th rowPersona Mayor
5th rowAdulto

Common Values

ValueCountFrequency (%)
Adulto 469031
71.6%
Adulto-Joven 94398
 
14.4%
Persona Mayor 83899
 
12.8%
Joven 7981
 
1.2%

Length

2023-06-01T12:16:47.213405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-01T12:16:47.299359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
adulto 469031
63.5%
adulto-joven 94398
 
12.8%
persona 83899
 
11.3%
mayor 83899
 
11.3%
joven 7981
 
1.1%

Most occurring characters

ValueCountFrequency (%)
o 833606
16.4%
A 563429
11.1%
d 563429
11.1%
u 563429
11.1%
l 563429
11.1%
t 563429
11.1%
n 186278
 
3.7%
e 186278
 
3.7%
r 167798
 
3.3%
a 167798
 
3.3%
Other values (8) 718651
14.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4065651
80.1%
Uppercase Letter 833606
 
16.4%
Dash Punctuation 94398
 
1.9%
Space Separator 83899
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 833606
20.5%
d 563429
13.9%
u 563429
13.9%
l 563429
13.9%
t 563429
13.9%
n 186278
 
4.6%
e 186278
 
4.6%
r 167798
 
4.1%
a 167798
 
4.1%
v 102379
 
2.5%
Other values (2) 167798
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
A 563429
67.6%
J 102379
 
12.3%
P 83899
 
10.1%
M 83899
 
10.1%
Dash Punctuation
ValueCountFrequency (%)
- 94398
100.0%
Space Separator
ValueCountFrequency (%)
83899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4899257
96.5%
Common 178297
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 833606
17.0%
A 563429
11.5%
d 563429
11.5%
u 563429
11.5%
l 563429
11.5%
t 563429
11.5%
n 186278
 
3.8%
e 186278
 
3.8%
r 167798
 
3.4%
a 167798
 
3.4%
Other values (6) 540354
11.0%
Common
ValueCountFrequency (%)
- 94398
52.9%
83899
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5077554
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 833606
16.4%
A 563429
11.1%
d 563429
11.1%
u 563429
11.1%
l 563429
11.1%
t 563429
11.1%
n 186278
 
3.7%
e 186278
 
3.7%
r 167798
 
3.3%
a 167798
 
3.3%
Other values (8) 718651
14.2%

Interactions

2023-06-01T12:16:35.049779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:01.065174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:03.183043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:04.984693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:06.832622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:08.746475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:10.568472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:12.445678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:14.357090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:16.283738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:18.101168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:19.881449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:21.842131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:23.724811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:25.724540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:27.554030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:29.552988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:31.373787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:33.169297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:35.154983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:01.184001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:03.275735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:05.080611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:06.925281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:08.841127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:10.666313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:12.546546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:14.555474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:16.380448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:18.194943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:19.980022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:21.941462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:23.894616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:25.821004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:27.650521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:29.647063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:31.467738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:33.265620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:35.267991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:01.303202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:03.375482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:05.182992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:07.037362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:08.943008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:10.772499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:12.658407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:14.655966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:16.482591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:18.300671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:20.083690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:22.051074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:24.002456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:25.924137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:27.756057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:29.750500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:31.568833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:33.368926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:35.367610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:01.410801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:03.467031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:05.276503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:07.137058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:09.035857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:10.866877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:12.754570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:14.749035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:16.575383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:18.391565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:20.178305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:22.145956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:24.103239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:26.016611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:27.851118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:29.842448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:31.660646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:33.463042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:35.476295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:01.516745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:03.567031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:05.378692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:07.237765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:09.137788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:10.972531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:12.860494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:14.860595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:16.676570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:18.492689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:20.280546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:22.248073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:24.218877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:26.121658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:27.954753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:29.945425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:31.760432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:33.566980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:35.700264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:01.622519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:03.667900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:05.482852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:07.336166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:09.237785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:11.076874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:12.966187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:14.963530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:16.778643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:18.592073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:20.383250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:22.352187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:24.326384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:26.222274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:28.058438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:30.046355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:31.861446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:33.670056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:35.802299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:01.738914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:03.762517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:05.580693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:07.431808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:09.332356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-01T12:16:29.251790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-01T12:16:32.885616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:34.751599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:36.944772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:02.987710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:04.781182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:06.637516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:08.547723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:10.363498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:12.240486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:14.146215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:16.083098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:17.906563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:19.682667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:21.531268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:23.506717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:25.510539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:27.355383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:29.347747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:31.175002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:32.972924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:34.845453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:37.046109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:03.085525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:04.875174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:06.733935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:08.641552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:10.458755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:12.338449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:14.246865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:16.177071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:18.001785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:19.777193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:21.731868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:23.607623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:25.610482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:27.449842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:29.445510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:31.271039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:33.067908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-01T12:16:34.942388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-01T12:16:47.393446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
idincomecurrent_address_months_countcustomer_agedays_since_requestzip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wcredit_risk_scorebank_months_countproposed_credit_limitsession_length_in_minutesmonthx1x2name_email_similaritydate_of_birth_distinct_emails_4wfraud_boolpayment_typeemployment_statushousing_statushas_other_cardsforeign_requestsourcedevice_oskeep_alive_sessionemail_is_freedevice_distinct_emails_8wphone_home_validphone_mobile_validsegmentacion_etaria
id1.0000.000-0.0010.0010.0010.0010.0010.000-0.000-0.0000.0000.0010.000-0.0000.001-0.0020.002-0.001-0.0010.0020.0000.0020.0020.0030.0000.0030.0010.0010.0000.0000.0020.0000.000
income0.0001.000-0.0250.076-0.023-0.073-0.099-0.111-0.115-0.0070.180-0.0330.135-0.0830.1240.0030.001-0.028-0.0270.0450.0290.0620.0840.0870.0120.0080.0390.0410.0240.0180.0090.0400.074
current_address_months_count-0.001-0.0251.0000.223-0.0050.0540.0270.0150.0290.0260.1260.0580.153-0.033-0.027-0.000-0.0010.047-0.2250.0430.0500.0620.1780.0710.0240.0110.0510.0560.0910.0170.1360.1070.106
customer_age0.0010.0760.2231.0000.044-0.027-0.0030.002-0.0080.0660.1390.0320.1230.0570.014-0.006-0.005-0.045-0.5520.0300.0520.1660.1790.1180.0130.0240.0920.0620.0390.0400.2490.1721.000
days_since_request0.001-0.023-0.0050.0441.000-0.0420.0600.0430.0180.024-0.083-0.013-0.0690.052-0.019-0.001-0.001-0.033-0.0640.0040.0760.0140.0280.0420.0000.0140.0160.0030.0150.0100.0450.0160.019
zip_count_4w0.001-0.0730.054-0.027-0.0421.0000.1260.1960.2660.011-0.0920.072-0.0230.041-0.265-0.0010.0030.0150.0980.0120.0540.0360.0360.0560.0250.0120.0210.0420.0280.0200.0860.0350.049
velocity_6h0.001-0.0990.027-0.0030.0600.1261.0000.4590.3870.017-0.1480.011-0.0580.063-0.398-0.0000.0020.0190.0700.0130.0610.0370.0420.0440.0090.0120.0400.0360.0430.0330.0520.0330.035
velocity_24h0.000-0.1110.0150.0020.0430.1960.4591.0000.5310.040-0.1480.003-0.0160.086-0.542-0.0000.0010.0240.0990.0100.0580.0400.0390.0580.0230.0130.0280.0520.0510.0430.0560.0400.033
velocity_4w-0.000-0.1150.029-0.0080.0180.2660.3870.5311.0000.046-0.1690.0210.0150.106-0.835-0.001-0.0000.0470.1530.0190.0610.0450.0600.0830.0340.0190.0500.1050.0520.0600.0930.0740.029
bank_branch_count_8w-0.000-0.0070.0260.0660.0240.0110.0170.0400.0461.000-0.0430.017-0.0260.015-0.051-0.001-0.001-0.016-0.0330.0170.0800.0230.0360.0610.0090.0110.0310.0150.0130.0110.0700.0350.046
credit_risk_score0.0000.1800.1260.139-0.083-0.092-0.148-0.148-0.169-0.0431.000-0.0750.653-0.0360.1640.0050.0040.066-0.1100.0690.0500.0620.1410.1360.0140.0170.0660.0390.0320.0420.0220.0350.084
bank_months_count0.001-0.0330.0580.032-0.0130.0720.0110.0030.0210.017-0.0751.000-0.0620.046-0.012-0.0010.003-0.007-0.0350.0320.0750.0650.0450.0530.0210.0140.0400.0450.0250.0240.0670.0470.060
proposed_credit_limit0.0000.1350.1530.123-0.069-0.023-0.058-0.0160.015-0.0260.653-0.0621.0000.004-0.0210.0030.0020.087-0.0480.0810.0510.0670.1600.1230.0260.0140.0600.0600.0430.0310.0490.0300.076
session_length_in_minutes-0.000-0.083-0.0330.0570.0520.0410.0630.0860.1060.015-0.0360.0460.0041.000-0.0970.000-0.0010.023-0.0570.0130.0300.0320.0290.1180.0100.0290.0310.0560.0430.0550.0380.0170.027
month0.0010.124-0.0270.014-0.019-0.265-0.398-0.542-0.835-0.0510.164-0.012-0.021-0.0971.000-0.000-0.001-0.035-0.1640.0200.0680.0460.0630.0870.0300.0190.0540.1190.0660.0600.1000.0790.031
x1-0.0020.003-0.000-0.006-0.001-0.001-0.000-0.000-0.001-0.0010.005-0.0010.0030.000-0.0001.0000.012-0.0040.0010.2620.0060.0030.0080.0120.0020.0000.0070.0110.0070.0030.0150.0040.006
x20.0020.001-0.001-0.005-0.0010.0030.0020.001-0.000-0.0010.0040.0030.002-0.001-0.0010.0121.000-0.0020.0020.2640.0030.0040.0070.0110.0050.0000.0060.0100.0070.0040.0150.0020.006
name_email_similarity-0.001-0.0280.047-0.045-0.0330.0150.0190.0240.047-0.0160.066-0.0070.0870.023-0.035-0.004-0.0021.0000.0420.0390.0420.0430.0450.0380.0230.0120.0450.0440.0760.0240.0270.0460.046
date_of_birth_distinct_emails_4w-0.001-0.027-0.225-0.552-0.0640.0980.0700.0990.153-0.033-0.110-0.035-0.048-0.057-0.1640.0010.0020.0421.0000.0290.0730.1480.0970.0580.0320.0230.0550.0560.0510.0420.2150.1370.253
fraud_bool0.0020.0450.0430.0300.0040.0120.0130.0100.0190.0170.0690.0320.0810.0130.0200.2620.2640.0390.0291.0000.0310.0290.0850.0370.0170.0000.0640.0410.0250.0350.0420.0050.024
payment_type0.0000.0290.0500.0520.0760.0540.0610.0580.0610.0800.0500.0750.0510.0300.0680.0060.0030.0420.0730.0311.0000.0560.1100.1630.0230.0170.0700.0340.0340.0300.0580.0720.045
employment_status0.0020.0620.0620.1660.0140.0360.0370.0400.0450.0230.0620.0650.0670.0320.0460.0030.0040.0430.1480.0290.0561.0000.1120.0390.0240.0300.0620.0690.0150.0500.1680.1750.218
housing_status0.0020.0840.1780.1790.0280.0360.0420.0390.0600.0360.1410.0450.1600.0290.0630.0080.0070.0450.0970.0850.1100.1121.0000.0750.0270.0180.0760.0580.0880.0290.1190.0930.234
has_other_cards0.0030.0870.0710.1180.0420.0560.0440.0580.0830.0610.1360.0530.1230.1180.0870.0120.0110.0380.0580.0370.1630.0390.0751.0000.0060.0130.0490.0870.0320.0310.0990.0210.112
foreign_request0.0000.0120.0240.0130.0000.0250.0090.0230.0340.0090.0140.0210.0260.0100.0300.0020.0050.0230.0320.0170.0230.0240.0270.0061.0000.0030.0550.0150.0300.0040.0100.0090.007
source0.0030.0080.0110.0240.0140.0120.0120.0130.0190.0110.0170.0140.0140.0290.0190.0000.0000.0120.0230.0000.0170.0300.0180.0130.0031.0000.0580.0620.0000.3160.0090.0180.022
device_os0.0010.0390.0510.0920.0160.0210.0400.0280.0500.0310.0660.0400.0600.0310.0540.0070.0060.0450.0550.0640.0700.0620.0760.0490.0550.0581.0000.0700.1500.0360.0760.0930.073
keep_alive_session0.0010.0410.0560.0620.0030.0420.0360.0520.1050.0150.0390.0450.0600.0560.1190.0110.0100.0440.0560.0410.0340.0690.0580.0870.0150.0620.0701.0000.0300.1000.0440.0240.045
email_is_free0.0000.0240.0910.0390.0150.0280.0430.0510.0520.0130.0320.0250.0430.0430.0660.0070.0070.0760.0510.0250.0340.0150.0880.0320.0300.0000.1500.0301.0000.0070.0130.0340.028
device_distinct_emails_8w0.0000.0180.0170.0400.0100.0200.0330.0430.0600.0110.0420.0240.0310.0550.0600.0030.0040.0240.0420.0350.0300.0500.0290.0310.0040.3160.0360.1000.0071.0000.0120.0640.030
phone_home_valid0.0020.0090.1360.2490.0450.0860.0520.0560.0930.0700.0220.0670.0490.0380.1000.0150.0150.0270.2150.0420.0580.1680.1190.0990.0100.0090.0760.0440.0130.0121.0000.2730.198
phone_mobile_valid0.0000.0400.1070.1720.0160.0350.0330.0400.0740.0350.0350.0470.0300.0170.0790.0040.0020.0460.1370.0050.0720.1750.0930.0210.0090.0180.0930.0240.0340.0640.2731.0000.143
segmentacion_etaria0.0000.0740.1061.0000.0190.0490.0350.0330.0290.0460.0840.0600.0760.0270.0310.0060.0060.0460.2530.0240.0450.2180.2340.1120.0070.0220.0730.0450.0280.0300.1980.1431.000

Missing values

2023-06-01T12:16:37.633714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-01T12:16:39.242312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idfraud_boolincomecurrent_address_months_countcustomer_agedays_since_requestpayment_typezip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wemployment_statuscredit_risk_scorehousing_statusbank_months_counthas_other_cardsproposed_credit_limitforeign_requestsourcesession_length_in_minutesdevice_oskeep_alive_sessionmonthx1x2name_email_similaritydate_of_birth_distinct_emails_4wemail_is_freedevice_distinct_emails_8wphone_home_validphone_mobile_validsegmentacion_etaria
21495850.00.8140.050.00.015659AA1269.02844.2600152789.8273553141.1373621605.0CF105.0BA9.01.0200.00.0INTERNET4.654872linux1.07.00.009336-2.0966820.1132084.01.01.01.01.0Adulto
4644860.00.9171.050.00.001409AB4430.05854.0657904210.0390955313.23394317.0CC208.0BA1.01.01500.00.0INTERNET3.720953linux1.01.02.229616-0.0058230.7927974.00.01.00.01.0Adulto
58252830.00.585.030.00.027292AB1698.07938.5257094963.1946195070.085151872.0CA138.0BE3.00.0200.00.0INTERNET2.912670windows1.03.0-0.1939450.8612070.86508210.00.01.00.01.0Adulto
98916360.00.6137.060.00.046158AA1512.07669.4941454251.2576825721.121146271.0CC33.0BB28.00.0200.00.0INTERNET20.889553linux0.02.02.1929600.4840680.3832382.01.01.01.01.0Persona Mayor
113950340.00.4156.040.00.032288AA941.06073.8569533653.0853584280.4979411322.0CA91.0BA28.00.0200.00.0INTERNET8.056782linux0.06.0-0.647299-0.3195440.9038355.01.01.01.01.0Adulto
123503240.00.361.050.00.010861AB2618.05697.3329653573.0925034329.13305241.0CB65.0BA25.00.0200.00.0INTERNET8.540440windows1.04.00.337705-1.6340930.0282848.00.01.01.01.0Adulto
138527330.00.1109.030.023.613744AB925.05709.4275293101.4075274913.06082912.0CB167.0BB31.00.0200.00.0INTERNET5.278296other1.03.01.039810-1.8917280.32984315.01.01.00.01.0Adulto
152145410.00.6171.020.00.011327AB470.02627.2483292989.1804403119.99488013.0CA117.0BE1.00.0200.00.0INTERNET1.304664linux1.07.0-0.497989-0.0509750.9029874.00.01.00.01.0Adulto-Joven
191580700.00.247.020.00.025274AB941.06622.5460675532.8750614190.39454036.0CA61.0BB9.01.0200.00.0INTERNET1.980974other1.05.0-1.190795-0.7085020.30326015.01.01.01.00.0Adulto-Joven
214349930.00.949.040.00.004121AB546.01539.9744424678.2447624312.13492330.0CA207.0BB30.00.0200.00.0INTERNET5.099331other1.06.0-0.2075010.7718620.1791352.00.01.00.01.0Adulto
idfraud_boolincomecurrent_address_months_countcustomer_agedays_since_requestpayment_typezip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wemployment_statuscredit_risk_scorehousing_statusbank_months_counthas_other_cardsproposed_credit_limitforeign_requestsourcesession_length_in_minutesdevice_oskeep_alive_sessionmonthx1x2name_email_similaritydate_of_birth_distinct_emails_4wemail_is_freedevice_distinct_emails_8wphone_home_validphone_mobile_validsegmentacion_etaria
1137181318890.00.7358.060.00.019061AD1091.03767.2167122652.7601074382.0794800.0CB97.0BA10.01.01000.00.0INTERNET3.833117linux1.05.00.7521180.0184950.1327325.00.01.01.01.0Persona Mayor
1137182120580.00.419.060.00.040269AB1208.08593.1147234745.9579774209.3856935.0CA107.0BB26.01.0200.00.0INTERNET3.724164windows0.05.0-1.498585-1.0093960.6318826.00.01.01.00.0Persona Mayor
11371831156710.00.9194.030.00.030739AD2327.01248.4048553606.6744654102.1169830.0CA41.0BE30.00.0200.00.0INTERNET5.903499windows0.05.0-1.3662981.0976450.50553011.00.01.00.01.0Adulto
11371858372690.00.8148.050.00.010308AB842.06852.7628844840.8398644437.81246039.0CF138.0BA20.00.0200.00.0INTERNET3.383891windows1.05.0-0.3372470.1055990.5352564.01.01.00.01.0Adulto
11371866174360.00.811.030.00.009451AB2816.07044.3207486149.5011666350.1434709.0CA85.0BC1.00.0200.00.0INTERNET4.413203linux1.00.00.5563210.8842730.2299769.01.01.00.01.0Adulto
11371877070630.00.1260.060.00.026023AB1795.02647.0653693186.0177234295.3115792.0CA185.0BE20.00.01000.00.0INTERNET5.780150windows0.05.00.397897-0.1035410.8701183.01.01.00.00.0Persona Mayor
11371887846060.00.530.050.00.052916AB1039.013107.1815127959.5972435463.1321935.0CE177.0BB25.01.0200.00.0INTERNET3.359270macintosh1.01.01.713050-1.9116690.0947155.00.01.01.01.0Adulto
11371892163750.00.80.030.00.005857AB918.01804.3781873607.5875174955.99397910.0CA215.0BC28.01.01500.00.0INTERNET4.821661windows0.03.0-0.727536-1.5409930.43702613.00.01.00.01.0Adulto
1137190418560.00.718.030.01.426644AA1188.04609.8740716271.0510814860.1186219.0CA122.0BC15.00.0200.00.0INTERNET2.899507other1.03.01.642429-0.0267830.44901015.00.01.01.01.0Adulto
11371919881910.00.956.030.00.006165AD1922.02608.6150314771.4996954950.83261142.0CA100.0BC5.01.0200.00.0INTERNET3.843607linux0.04.0-1.6622471.5762140.76995910.01.01.01.01.0Adulto